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An Efficient Method for Computing Single-Parameter Partial Expected Value of Perfect Information

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  • Mark Strong
  • Jeremy E. Oakley

Abstract

The value of learning an uncertain input in a decision model can be quantified by its partial expected value of perfect information (EVPI). This is commonly estimated via a 2-level nested Monte Carlo procedure in which the parameter of interest is sampled in an outer loop, and then conditional on this sampled value, the remaining parameters are sampled in an inner loop. This 2-level method can be difficult to implement if the joint distribution of the inner-loop parameters conditional on the parameter of interest is not easy to sample from. We present a simple alternative 1-level method for calculating partial EVPI for a single parameter that avoids the need to sample directly from the potentially problematic conditional distributions. We derive the sampling distribution of our estimator and show in a case study that it is both statistically and computationally more efficient than the 2-level method.

Suggested Citation

  • Mark Strong & Jeremy E. Oakley, 2013. "An Efficient Method for Computing Single-Parameter Partial Expected Value of Perfect Information," Medical Decision Making, , vol. 33(6), pages 755-766, August.
    • Handle: RePEc:sae:medema:v:33:y:2013:i:6:p:755-766
    DOI: 10.1177/0272989X12465123
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    References listed on IDEAS

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    1. Karl Claxton & John Posnett, 1996. "An economic approach to clinical trial design and research priority‐setting," Health Economics, John Wiley & Sons, Ltd., vol. 5(6), pages 513-524, November.
    2. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
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    Cited by:

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    2. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    3. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    4. Emanuele Borgonovo & Alessandra Cillo & Curtis L. Smith, 2018. "On the Relationship between Safety and Decision Significance," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1541-1558, August.
    5. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    6. Tianyang Wang & James S. Dyer & Warren J. Hahn, 2017. "Sensitivity analysis of decision making under dependent uncertainties using copulas," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 117-139, November.
    7. Elmar Plischke & Emanuele Borgonovo, 2020. "Fighting the Curse of Sparsity: Probabilistic Sensitivity Measures From Cumulative Distribution Functions," Risk Analysis, John Wiley & Sons, vol. 40(12), pages 2639-2660, December.
    8. Haag, Fridolin & Chennu, Arjun, 2023. "Assessing whether decisions are more sensitive to preference or prediction uncertainty with a value of information approach," Omega, Elsevier, vol. 121(C).
    9. Emanuele Borgonovo & Alessandra Cillo, 2017. "Deciding with Thresholds: Importance Measures and Value of Information," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1828-1848, October.

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